Efficient Low-resolution Character Recognition Using Sub-machine-code Genetic Programming

  • Giovanni Adorni
  • Stefano Cagnoni
  • Marco Gori
  • Monica Mordonini
Part of the Advances in Soft Computing book series (AINSC, volume 18)


The paper describes an approach to low-resolution character recognition for real-time applications based on a set of binary classifiers designed by means of Sub-machine-code Genetic Programming (SmcGP). SmcGP is a type of GP that interprets long integers as bit strings to achieve SIMD processing on traditional sequential computers. The method was tested on an extensive set of very low-resolution binary patterns (of size 13 × 8 pixels) that represent digits from 0 to 9. Ten binary classifiers were designed, each corresponding to a pattern class. In case of no response by any of the classifiers, a reference LVQ classifier was used. The paper compares the resulting classifier with a reference classifier, showing an almost 10-fold improvement in speed, at the price of a slightly lower accuracy.


Genetic Programming Input Pattern Binary Classifier Optical Character Recognition License Plate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Giovanni Adorni
    • 1
  • Stefano Cagnoni
    • 2
  • Marco Gori
    • 3
  • Monica Mordonini
    • 2
  1. 1.Dipartimento di Informatica, Sistemistica e TelematicaUniversità di GenovaItaly
  2. 2.Dipartimento di Ingegneria dell’InformazioneUniversità di ParmaItaly
  3. 3.Dipartimento di Ingegneria dell’InformazioneUniversità di SienaItaly

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